Abstract

To get an effective control of large river basin systems, the decision maker wishes to develop optimal operating policies. To establish these policies, the future behavior of inputs, such as available resources, demand to be satisfied, etc., must be known or rather predicted. Because of the uncertainties inherent in water resources (WR) processes, both in quantity and quality, the prediction scheme to be constructed should be able to handle stochastic effects. Moreover, the algorithms should be recursive to avoid cumbersome computations and to be able to be used for real-time forecasting. This is especially important in case of emergency, e.g. flash floods.

A general state space based formulation of water resources systems is given. It is shown that the general model of runoff control systems is able to handle different kinds of uncertainties. Optimal sequential prediction algorithms for linear discrete time stochastic WR system are presented. In the framework of runoff control the case of optimal stochastic dynamic water quality control is discussed and feedback control policies are established.

The algorithms proposed might help the decision maker in working out the optimal operating policies for a large river basin system in the presence of different kinds of uncertainties.